US7062433B2 - Method of speech recognition with compensation for both channel distortion and background noise - Google Patents

Method of speech recognition with compensation for both channel distortion and background noise Download PDF

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US7062433B2
US7062433B2 US10/051,640 US5164002A US7062433B2 US 7062433 B2 US7062433 B2 US 7062433B2 US 5164002 A US5164002 A US 5164002A US 7062433 B2 US7062433 B2 US 7062433B2
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Yifan Gong
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Texas Instruments Inc
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/20—Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/14—Speech classification or search using statistical models, e.g. Hidden Markov Models [HMMs]
    • G10L15/142—Hidden Markov Models [HMMs]
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise

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  • This invention relates to speech recognition and more particularly to compensation for both background noise and channel distortion.
  • a speech recognizer trained with relatively a quiet office environment speech data and then operating in a mobile environment may fail due to at least to the two distortion sources of back ground noise and microphone changes.
  • the background noise may, for example, be from a computer fan, car engine, and/or road noise.
  • the microphone changes may be due to the quality of the microphone, whether the microphone is hand-held or hands-free and, the position of the microphone to the mouth. In mobile applications of speech recognition, both the microphone conditions and background noise are subject to change.
  • Cepstral Mean Normalization removes utterance mean and is a simple and effective way of dealing with convolutive distortion such as telephone channel distortion. See “Effectiveness of Linear Prediction Characteristics of the Speech Wave for Automatic Speaker Identification and Verification” of B. Atal in Journal of Acoustics Society of America, Vol. 55: 1304–1312, 1974.
  • Spectral Subtraction reduces background noise in the feature space. See article “Suppression of Acoustic Noise in Speech Using Spectral Subtraction” of S. F. Boll in IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-27(2): 113–129, April 1979.
  • Parallel Model Combination gives an approximation of speech models in noisy conditions from noise-free speech models and noise estimates.
  • Joint compensation of additive noise and convolutive noise can be achieved by the introduction of a channel model and a noise model.
  • a spectral bias for additive noise and a cepstral bias for convolutive noise are introduced in an article by M. Afify, Y. Gong, and J. P. Haton. This article is entitled “A General Joint Additive and Convolutive Bias Compensation Approach Applied to noisy Lombard Speech Recognition” in IEEE Trans. on Speech and Audio Processing, 6(6): 524–538, November 1998.
  • the two biases can be calculated by application of Expectation Maximization (EM) in both spectral and convolutive domains.
  • EM Expectation Maximization
  • Gauvain, et al is presented to calculate the convolutive component, which requires rescanning of training data. See J. L. Gauvain, L. Lamel, M. Adda-Decker, and D. Matrouf entitled “Developments in Continuous Speech Dictation using the ARPA NAB News Task.” In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 73–76, Detroit, 1996. Solution of the convolutive component by a steepest descent method has also been reported. See Y. Minami and S. Furui entitled “A Maximum Likelihood Procedure for a Universal Adaptation Method Based on HMM Composition.” See Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, pages 129–132, Detroit, 1995.
  • the nonlinear changes of both type of distortions can be approximated by linear equations, assuming that the changes are small.
  • a Jacobian approach which models speech model parameter changes as the product of a jacobian matrix and the difference in noisy conditions, and statistical linear approximation are along this direction. See S. Sagayama, Y. Yamaguchi, and S. Takahashi entitled “Jacobian Adaptation of noisy Speech Models,” in Proceedings of IEEE Automatic Speech Recognition Workshop, pages 396–403, Santa Barbara, Calif., USA, December 1997. IEEE Signal Processing Society. Also see “Statistical Linear Approximation for Environment Compensation” of N. S. Kim, IEEE Signal Processing Letters, 5(1): 8–10, January 1998.
  • MLLR Maximum Likelihood Linear Regression
  • a new method is disclosed that simultaneously handles noise and channel distortions to make a speaker independent system robust to a wide variety of noises and channel distortions.
  • FIG. 1 illustrates a speech recognizer according to one embodiment of the present invention
  • FIG. 2 illustrates the method of the present invention.
  • FIG. 1 there is illustrated a speech recognizer according to the present invention.
  • the speech is applied to recognizer 11 .
  • the speech is compared to Hidden Markov Models (HMM) 13 to recognize the text.
  • HMM Hidden Markov Models
  • the models initially provided are those based on speech recorded in a quiet environment with a microphone of good quality.
  • a speech model set is provided using statistics about the noise and speech.
  • the first Step 1 is to start with HMM models trained on clean speech, with cepstral mean normalization. We modify these models to get models to compensate for channel/microphone distortion (convolutive distortion) and simultaneous background noise (additive distortion).
  • the HMM modeling method of this invention represents the acoustic probability density function (PDF) corresponding to each HMM state as a mixture of Gaussian components, as is well known in the art.
  • Such HMM models have many parameters, such as Gaussian component mean vectors, covariances, and mixture component weights for each state, as well as HMM state transition probabilities.
  • the method of this invention teaches modifying the mean vectors m pj,k of the original model space, where p is the index of the HMM, j is the state and k is the mixing component.
  • the second Step 2 is to calculate the mean mel-scaled cesptrum coefficients (MFCC) vector over the trained database. Scan all data and calculate the mean to get b.
  • MFCC mel-scaled cesptrum coefficients
  • m p,j,k m p,j,k +b.
  • the fourth Step 4 is for a given input test utterance, an estimate of the background noise vector ⁇ tilde over (X) ⁇ is calculated.
  • Step 5 we calculate the mean vectors adapted to the noise ⁇ tilde over (X) ⁇ using equation 4.
  • ⁇ circumflex over (m) ⁇ p,j,k IDFT(DFT( ⁇ overscore (m) ⁇ p,j,k ) ⁇ DFT( ⁇ tilde over (X) ⁇ )).
  • DFT and IDFT are, respectively, the DFT and inverse DFT operation
  • ⁇ circumflex over (m) ⁇ p,j,k is the noise compensated mean vector.
  • Equation 4 involves several operators.
  • DFT is the Discrete Fourier Transform and IDFT is the Inverse Discrete Fourier Transform, which are respectively used to convert from the cepstrum domain to the log spectrum domain, and vice versa.
  • the ⁇ is an operation applied to two log spectral vectors to produce a log spectral vector representing the linear sum of spectra.
  • the operation ⁇ is defined by equations 2 and 3.
  • Equation 2 defines the operation ⁇ which operates on two D dimensional vectors u and v and the result is a vector of D dimensions, [ w 1 l , w 2 l , . . . w D l ] T where T is the transposition.
  • Equation 3. defines the jth element in that vector ( w j l ). This completes the definition of Equation 4.
  • H be the variable denoting HMM index
  • J be the variable for state index
  • K be the variable for mixing component index
  • Equation 7 shows that ⁇ circumflex over (b) ⁇ can be worked out analytically, and it is not necessary to do the physical generation and integration. The final result is represented by Equation 7 which reduces the integration into sums over HMMs, over states and over mixing components.
  • This resulting target model means are the desired modified parameters of the HMM models used in the recognizer. This operation is done for each utterance.
  • FIG. 2 illustrates that for a next utterance (Step 8 ) the process starts with step 4 .
  • Eq-10 consists in averaging the compensated mean vectors ⁇ circumflex over (m) ⁇ p,j,k .
  • the model ⁇ dot over (m) ⁇ p,j,k, of Eq-8 is then used with CMN on noisy speech.
  • a database containing recordings in a car was used.
  • HMMs used in all experiments were trained using clean speech data. Utterance-based cepstral mean normalization was used.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Probability & Statistics with Applications (AREA)
  • Circuit For Audible Band Transducer (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Time-Division Multiplex Systems (AREA)
  • Complex Calculations (AREA)
US10/051,640 2001-03-14 2002-01-18 Method of speech recognition with compensation for both channel distortion and background noise Expired - Lifetime US7062433B2 (en)

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US20050182621A1 (en) * 2004-01-12 2005-08-18 Igor Zlokarnik Automatic speech recognition channel normalization
US20050228669A1 (en) * 2004-04-12 2005-10-13 Bernard Alexis P Method to extend operating range of joint additive and convolutive compensating algorithms
US20050256706A1 (en) * 2001-03-20 2005-11-17 Microsoft Corporation Removing noise from feature vectors
US20070239448A1 (en) * 2006-03-31 2007-10-11 Igor Zlokarnik Speech recognition using channel verification
US20080052074A1 (en) * 2006-08-25 2008-02-28 Ramesh Ambat Gopinath System and method for speech separation and multi-talker speech recognition
US20090144059A1 (en) * 2007-12-03 2009-06-04 Microsoft Corporation High performance hmm adaptation with joint compensation of additive and convolutive distortions
US20090177468A1 (en) * 2008-01-08 2009-07-09 Microsoft Corporation Speech recognition with non-linear noise reduction on mel-frequency ceptra
US20100070280A1 (en) * 2008-09-16 2010-03-18 Microsoft Corporation Parameter clustering and sharing for variable-parameter hidden markov models
US20100191524A1 (en) * 2007-12-18 2010-07-29 Fujitsu Limited Non-speech section detecting method and non-speech section detecting device
US8639502B1 (en) 2009-02-16 2014-01-28 Arrowhead Center, Inc. Speaker model-based speech enhancement system
US20140278412A1 (en) * 2013-03-15 2014-09-18 Sri International Method and apparatus for audio characterization
US20210201928A1 (en) * 2019-12-31 2021-07-01 Knowles Electronics, Llc Integrated speech enhancement for voice trigger application

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US20040148160A1 (en) * 2003-01-23 2004-07-29 Tenkasi Ramabadran Method and apparatus for noise suppression within a distributed speech recognition system
JP4357867B2 (ja) * 2003-04-25 2009-11-04 パイオニア株式会社 音声認識装置、音声認識方法、並びに、音声認識プログラムおよびそれを記録した記録媒体
JP2006084732A (ja) * 2004-09-15 2006-03-30 Univ Of Tokyo 多項式近似に基づく雑音下音声認識のためのモデル適応法
US20070033027A1 (en) * 2005-08-03 2007-02-08 Texas Instruments, Incorporated Systems and methods employing stochastic bias compensation and bayesian joint additive/convolutive compensation in automatic speech recognition
CN1897109B (zh) * 2006-06-01 2010-05-12 电子科技大学 一种基于mfcc的单一音频信号识别方法
CN101030369B (zh) * 2007-03-30 2011-06-29 清华大学 基于子词隐含马尔可夫模型的嵌入式语音识别方法
US8214215B2 (en) * 2008-09-24 2012-07-03 Microsoft Corporation Phase sensitive model adaptation for noisy speech recognition
EP2182512A1 (en) * 2008-10-29 2010-05-05 BRITISH TELECOMMUNICATIONS public limited company Speaker verification
CN103811008A (zh) * 2012-11-08 2014-05-21 中国移动通信集团上海有限公司 一种音频内容识别方法和装置
CN106057195A (zh) * 2016-05-25 2016-10-26 东华大学 一种基于嵌入式音频识别的无人机探测系统
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US7310599B2 (en) * 2001-03-20 2007-12-18 Microsoft Corporation Removing noise from feature vectors
US7451083B2 (en) 2001-03-20 2008-11-11 Microsoft Corporation Removing noise from feature vectors
US20050256706A1 (en) * 2001-03-20 2005-11-17 Microsoft Corporation Removing noise from feature vectors
US20050273325A1 (en) * 2001-03-20 2005-12-08 Microsoft Corporation Removing noise from feature vectors
US7797157B2 (en) * 2004-01-12 2010-09-14 Voice Signal Technologies, Inc. Automatic speech recognition channel normalization based on measured statistics from initial portions of speech utterances
US20050182621A1 (en) * 2004-01-12 2005-08-18 Igor Zlokarnik Automatic speech recognition channel normalization
US20050228669A1 (en) * 2004-04-12 2005-10-13 Bernard Alexis P Method to extend operating range of joint additive and convolutive compensating algorithms
US7236930B2 (en) * 2004-04-12 2007-06-26 Texas Instruments Incorporated Method to extend operating range of joint additive and convolutive compensating algorithms
US20070239448A1 (en) * 2006-03-31 2007-10-11 Igor Zlokarnik Speech recognition using channel verification
US8346554B2 (en) 2006-03-31 2013-01-01 Nuance Communications, Inc. Speech recognition using channel verification
US7877255B2 (en) * 2006-03-31 2011-01-25 Voice Signal Technologies, Inc. Speech recognition using channel verification
US20110004472A1 (en) * 2006-03-31 2011-01-06 Igor Zlokarnik Speech Recognition Using Channel Verification
US20080052074A1 (en) * 2006-08-25 2008-02-28 Ramesh Ambat Gopinath System and method for speech separation and multi-talker speech recognition
US7664643B2 (en) 2006-08-25 2010-02-16 International Business Machines Corporation System and method for speech separation and multi-talker speech recognition
US8180637B2 (en) 2007-12-03 2012-05-15 Microsoft Corporation High performance HMM adaptation with joint compensation of additive and convolutive distortions
US20090144059A1 (en) * 2007-12-03 2009-06-04 Microsoft Corporation High performance hmm adaptation with joint compensation of additive and convolutive distortions
US20100191524A1 (en) * 2007-12-18 2010-07-29 Fujitsu Limited Non-speech section detecting method and non-speech section detecting device
US8326612B2 (en) * 2007-12-18 2012-12-04 Fujitsu Limited Non-speech section detecting method and non-speech section detecting device
US8798991B2 (en) 2007-12-18 2014-08-05 Fujitsu Limited Non-speech section detecting method and non-speech section detecting device
US20090177468A1 (en) * 2008-01-08 2009-07-09 Microsoft Corporation Speech recognition with non-linear noise reduction on mel-frequency ceptra
US8306817B2 (en) 2008-01-08 2012-11-06 Microsoft Corporation Speech recognition with non-linear noise reduction on Mel-frequency cepstra
US20100070280A1 (en) * 2008-09-16 2010-03-18 Microsoft Corporation Parameter clustering and sharing for variable-parameter hidden markov models
US8145488B2 (en) * 2008-09-16 2012-03-27 Microsoft Corporation Parameter clustering and sharing for variable-parameter hidden markov models
US8639502B1 (en) 2009-02-16 2014-01-28 Arrowhead Center, Inc. Speaker model-based speech enhancement system
US20140278412A1 (en) * 2013-03-15 2014-09-18 Sri International Method and apparatus for audio characterization
US9489965B2 (en) * 2013-03-15 2016-11-08 Sri International Method and apparatus for acoustic signal characterization
US20210201928A1 (en) * 2019-12-31 2021-07-01 Knowles Electronics, Llc Integrated speech enhancement for voice trigger application

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DE60212477D1 (de) 2006-08-03
EP1241662A2 (en) 2002-09-18
EP1241662A3 (en) 2004-02-18
JP2002311989A (ja) 2002-10-25
US20020173959A1 (en) 2002-11-21
EP1241662B1 (en) 2006-06-21
DE60212477T2 (de) 2007-07-05

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